Multiple Imputation for Longitudinal Data Under a Bayesian Multilevel Model

DOI10.1080/03610920902947162zbMath1176.62022OpenAlexW2100228569MaRDI QIDQ3645008

Hakan Demirtas

Publication date: 16 November 2009

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920902947162

zbMATH Keywords

longitudinal datamissing datamultiple imputationpattern-mixture modelsmixed-models

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)

Cites Work

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• Random-Effects Models for Longitudinal Data
• On the performance of bias-reduction techniques for variance estimation in approximate Bayesian bootstrap imputation
• On imputing continuous data when the eventual interest pertains to ordinalized outcomes via threshold concept
• Imputing continuous data under some non‐Gaussian distributions
• The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence
• Simulation driven inferences for multiply imputed longitudinal datasets*
• A Random-Effects Ordinal Regression Model for Multilevel Analysis
• Practical Advice on How to Impute Continuous Data When the Ultimate Interest Centers on Dichotomized Outcomes Through Pre-Specified Thresholds
• Plausibility of multivariate normality assumption when multiply imputing non-Gaussian continuous outcomes: a simulation assessment
• A method for multivariate ordinal data generation given marginal distributions and correlations
• Longitudinal Data Analysis

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